{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d379b91c-3e83-4b4a-94f8-631d0dee8438",
   "metadata": {},
   "source": [
    "## 从三体运动到太阳黑子变化预测\n",
    "\n",
    "### 前言\n",
    "\n",
    "太阳黑子是太阳光球层上发生的太阳活动现象，通常成群出现。 预测太阳黑子变化是空间气象研究中最活跃的领域之一。 \n",
    "\n",
    "太阳黑子观测持续时间很长。 长时间的数据积累有利于挖掘太阳黑子变化的规律。 长期观测显示,太阳黑子数及面积变化呈现出明显的周期 性，且周期呈现不规则性,大致范围在 9 ~ 13 a[2] , 平均周期约为 11 a,太阳黑子数及面积变化的峰 值不恒定。 最新数据显示,近些年来太阳黑子数和面积有明显的下降趋势。\n",
    "\n",
    "![Hathaway_Cycle_24_Prediction](images/Hathaway_Cycle_24_Prediction.png)\n",
    "\n",
    "鉴于太阳黑子活动强烈程度对地球有着深刻的影响，因此探测太阳黑子活动就显得尤为重要。基于物理学模型(如动力模型[3] )和统计学模型 (如自回归滑动平均[4] )已被广泛应用于探测太阳黑子活动。 为了更高效地捕捉太阳黑子时间序列中存在的非线性关系,机器学习方法被引入。\n",
    "\n",
    "值得一提的是,机器学习中的神经网络更擅长挖掘数据中的非线性关系。\n",
    "\n",
    "**因此，本文将介绍如何使用时序数据库`CnosDB`存储太阳黑子变化数据，并进一步使用TensorFlow实现`1DConv+LSTM` 网络来预测太阳黑子数量变化。**\n",
    "\n",
    "\n",
    "### 太阳黑子变化观测数据集简介\n",
    "\n",
    "本文使用的太阳黑子数据集是由SILSO 网站发布2.0版本 (WDC-SILSO, Royal Observatory of Belgium, Brussels,http://sidc.be/silso/datafiles)\n",
    "\n",
    "![sunspot_dataset.png](images/sunspot_dataset.png)\n",
    "\n",
    "\n",
    "我们主要分析和探索：1749至2023年，月均太阳黑子数(monthly mean sunspot number，MSSN)变化情况。\n",
    "\n",
    "\n",
    "#### MSSN 数据分析和探索\n",
    "\n",
    "将 MSSN 数据 csv 格式文件`SN_m_tot_V2.0.csv`（https://www.sidc.be/silso/infosnmtot） 下载到本地。\n",
    "\n",
    "以下是官方提供的CSV文件描述：\n",
    "\n",
    "```\n",
    "Filename: SN_m_tot_V2.0.csv\n",
    "Format: Comma Separated values (adapted for import in spreadsheets)\n",
    "The separator is the semicolon ';'.\n",
    "\n",
    "Contents:\n",
    "Column 1-2: Gregorian calendar date\n",
    "- Year\n",
    "- Month\n",
    "Column 3: Date in fraction of year.\n",
    "Column 4: Monthly mean total sunspot number.\n",
    "Column 5: Monthly mean standard deviation of the input sunspot numbers.\n",
    "Column 6: Number of observations used to compute the monthly mean total sunspot number.\n",
    "Column 7: Definitive/provisional marker. '1' indicates that the value is definitive. '0' indicates that the value is still provisional.\n",
    "```\n",
    "\n",
    "我们使用 `pandas` 进行文件加载和预览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5343fff-a7a7-44b0-9fd2-a3b5207e79f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>date_fraction</th>\n",
       "      <th>mssn</th>\n",
       "      <th>standard_deviation</th>\n",
       "      <th>observations</th>\n",
       "      <th>marker</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1749</td>\n",
       "      <td>1</td>\n",
       "      <td>1749.042</td>\n",
       "      <td>96.7</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1749-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1749</td>\n",
       "      <td>2</td>\n",
       "      <td>1749.123</td>\n",
       "      <td>104.3</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1749-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1749</td>\n",
       "      <td>3</td>\n",
       "      <td>1749.204</td>\n",
       "      <td>116.7</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1749-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1749</td>\n",
       "      <td>4</td>\n",
       "      <td>1749.288</td>\n",
       "      <td>92.8</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1749-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1749</td>\n",
       "      <td>5</td>\n",
       "      <td>1749.371</td>\n",
       "      <td>141.7</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1749-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year month  date_fraction   mssn  standard_deviation  observations  marker  \\\n",
       "0  1749     1       1749.042   96.7                -1.0            -1       1   \n",
       "1  1749     2       1749.123  104.3                -1.0            -1       1   \n",
       "2  1749     3       1749.204  116.7                -1.0            -1       1   \n",
       "3  1749     4       1749.288   92.8                -1.0            -1       1   \n",
       "4  1749     5       1749.371  141.7                -1.0            -1       1   \n",
       "\n",
       "     date  \n",
       "0  1749-1  \n",
       "1  1749-2  \n",
       "2  1749-3  \n",
       "3  1749-4  \n",
       "4  1749-5  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"SN_m_tot_V2.0.csv\", sep=\";\", header=None)\n",
    "df.columns = [\"year\", \"month\", \"date_fraction\", \"mssn\", \"standard_deviation\", \"observations\", \"marker\"]\n",
    "\n",
    "# convert year and month to strings\n",
    "df[\"year\"] = df[\"year\"].astype(str)\n",
    "df[\"month\"] = df[\"month\"].astype(str)\n",
    "\n",
    "# concatenate year and month\n",
    "df[\"date\"] = df[\"year\"] + \"-\" + df[\"month\"]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "044b8158-3455-4978-bddd-e08229849cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "\n",
    "df[\"Date\"] = pd.to_datetime(df[\"date\"], format=\"%Y-%m\")\n",
    "plt.plot(df[\"Date\"], df[\"mssn\"])\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"MSSN\")\n",
    "plt.title(\"Sunspot Activity Over Time\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61bebf4-8e7d-45cd-8783-6db0daaa2944",
   "metadata": {},
   "source": [
    "### 使用时序数据库 CnosDB 存储 MSSN 数据\n",
    "\n",
    "#### CnosDB 简介\n",
    "\n",
    "CnosDB（An Open Source Distributed Time Series Database with high performance, high compression ratio and high usability.）\n",
    "\n",
    "- Official Website: http://www.cnosdb.com\n",
    "- Github Repo: https://github.com/cnosdb/cnosdb\n",
    "\n",
    "（注：本文假设你已具备 CnosDB 安装部署和基本使用能力，相关文档详见 https://docs.cnosdb.com/）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be9109f-ead2-4f95-93d5-02384f611823",
   "metadata": {},
   "source": [
    "在命令行中使用 Docker 启动 CnosDB 数据库服务，并进入容器使用 CnosDB CLI 工具直接访问 CnosDB：\n",
    "```SHELL\n",
    "(base) root@ecs-django-dev:~# docker run --restart=always --name cnosdb -d --env cpu=2 --env memory=4 -p 31007:31007 cnosdb/cnosdb:v2.0.2.1-beta\n",
    "\n",
    "(base) root@ecs-django-dev:~# docker exec -it cnosdb sh sh\n",
    "# cnosdb-cli\n",
    "CnosDB CLI v2.0.0\n",
    "Input arguments: Args { host: \"0.0.0.0\", port: 31007, user: \"cnosdb\", password: None, database: \"public\", target_partitions: None, data_path: None, file: [], rc: None, format: Table, quiet: false }\n",
    "```\n",
    "\n",
    "为了简化分析，我们只需存储数据集中观测时间和太阳黑子数。因此，我们将年（Col 0）和月（Col 1）拼接作为观测时间（date, 字符串类型），月均太阳黑子数（Col 3）可以不作处理直接存储。\n",
    "\n",
    "我们可以在 CnosDB CLI 中使用 SQL 创建一张名为 `sunspot` 数据表，以用于存储 MSSN 数据集。\n",
    "\n",
    "```SQL\n",
    "public ❯ CREATE TABLE sunspot (\n",
    "    date STRING,\n",
    "    mssn DOUBLE,\n",
    ");\n",
    "Query took 0.002 seconds.\n",
    "\n",
    "public ❯ SHOW TABLES;\n",
    "+---------+\n",
    "| Table   |\n",
    "+---------+\n",
    "| sunspot |\n",
    "+---------+\n",
    "Query took 0.001 seconds.\n",
    "\n",
    "public ❯ SELECT * FROM sunspot;\n",
    "+------+------+------+\n",
    "| time | date | mssn |\n",
    "+------+------+------+\n",
    "+------+------+------+\n",
    "Query took 0.002 seconds.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70eed970-4fc2-408a-a1bf-77621d76d450",
   "metadata": {},
   "source": [
    "#### 使用 CnosDB Python Connector 连接和读写 CnosDB 数据库\n",
    "\n",
    "Github Repo: https://github.com/cnosdb/cnosdb-client-python\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52db8a83-2a54-41aa-bc38-dd6aee07855d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: cnos-connector in /Users/subsegment/opt/anaconda3/envs/tf/lib/python3.9/site-packages (0.1.8)\r\n"
     ]
    }
   ],
   "source": [
    "# 安装 Python Connector\n",
    "!pip install -U cnos-connector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df0d595f-3193-4892-b89c-a1ec83f81d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cnosdb_connector import connect\n",
    "\n",
    "conn = connect(url=\"http://127.0.0.1:8902/\", user=\"root\", password=\"\")\n",
    "cursor = conn.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf01d8c-53ed-41a1-a46e-02c4421ab9ce",
   "metadata": {},
   "source": [
    "如果不习惯使用 CnosDB CLI，我们也可以直接使用 Python Connector 创建数据表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7407293-56ec-4213-b150-e3787a47b485",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建 tf_demo database\n",
    "conn.create_database(\"tf_demo\")\n",
    "# 使用 tf_demo database\n",
    "conn.switch_database(\"tf_demo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cfcba29c-5333-44e7-874f-704ffadf5c78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Database': 'tf_demo'}, {'Database': 'usage_schema'}, {'Database': 'public'}]\n"
     ]
    }
   ],
   "source": [
    "print(conn.list_database())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "382a195c-855e-4cfd-9e64-e3d9c0d2611f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建 sunspot table\n",
    "cursor.execute(\"CREATE TABLE sunspot (date STRING, mssn DOUBLE,);\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9cc9c1c-95ac-4a0e-afb2-1b337276f0b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Table': 'sunspot'}]\n"
     ]
    }
   ],
   "source": [
    "print(conn.list_table())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d7c6e6-74e9-4160-be34-bac1978bd336",
   "metadata": {},
   "source": [
    "#### 将 MSSN 数据写入到 CnosDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e587f2e8-e353-4795-a8fb-97dca1130180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1749-3</td>\n",
       "      <td>1749-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1749</td>\n",
       "      <td>4</td>\n",
       "      <td>1749.288</td>\n",
       "      <td>92.8</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1749-4</td>\n",
       "      <td>1749-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1749</td>\n",
       "      <td>5</td>\n",
       "      <td>1749.371</td>\n",
       "      <td>141.7</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1749-5</td>\n",
       "      <td>1749-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year month  date_fraction   mssn  standard_deviation  observations  marker  \\\n",
       "0  1749     1       1749.042   96.7                -1.0            -1       1   \n",
       "1  1749     2       1749.123  104.3                -1.0            -1       1   \n",
       "2  1749     3       1749.204  116.7                -1.0            -1       1   \n",
       "3  1749     4       1749.288   92.8                -1.0            -1       1   \n",
       "4  1749     5       1749.371  141.7                -1.0            -1       1   \n",
       "\n",
       "     date       Date  \n",
       "0  1749-1 1749-01-01  \n",
       "1  1749-2 1749-02-01  \n",
       "2  1749-3 1749-03-01  \n",
       "3  1749-4 1749-04-01  \n",
       "4  1749-5 1749-05-01  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ade6a8d-baa6-404d-a6d7-0b6e1a40b381",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     date   mssn                        time\n",
      "0  1749-1   96.7  2023-03-02T11:56:42.742826\n",
      "1  1749-2  104.3  2023-03-02T11:56:42.798554\n",
      "2  1749-3  116.7  2023-03-02T11:56:42.817468\n",
      "3  1749-4   92.8  2023-03-02T11:56:42.834098\n",
      "4  1749-5  141.7  2023-03-02T11:56:42.849295\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_q/x9yykrrd46b0czsv4rcqcnp40000gn/T/ipykernel_6542/3951511477.py:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(\"select * from sunspot;\", conn)\n"
     ]
    }
   ],
   "source": [
    "conn.write_dataframe(df, \"sunspot\", ['date', 'mssn'])\n",
    "\n",
    "df = pd.read_sql(\"select * from sunspot;\", conn)\n",
    "\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4dace7-88f4-49ca-a5fd-eb6e89061260",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 使用 TensorFlow 复现 1DConv+LSTM 网络，预测太阳黑子变化\n",
    "\n",
    "参考论文：[程术, 石耀霖, and 张怀. \"基于神经网络预测太阳黑子变化.\" (2022).\n",
    "](http://journal.ucas.ac.cn/CN/10.7523/j.ucas.2021.0068)\n",
    "\n",
    "![fig1](images/MSSN.png)\n",
    "\n",
    "\n",
    "\n",
    "##### 下文将使用TensorFlow 复现论文中的方法，即 `1DConv+LSTM` 神经网络，并根据实际数据来预测月均太阳黑子数（MSSN）变化。\n",
    "\n",
    "\n",
    "首先将数据集划分为训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e66569a-f687-4eeb-8f9c-7d71e1478324",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# Convert the data values to numpy for better and faster processing\n",
    "time_index = np.array(df['date'])\n",
    "data = np.array(df['mssn'])   \n",
    "\n",
    "# ratio to split the data\n",
    "SPLIT_RATIO = 0.8\n",
    "\n",
    "# Dividing into train-test split\n",
    "split_index = int(SPLIT_RATIO * data.shape[0])   \n",
    "\n",
    "# Train-Test Split\n",
    "train_data = data[:split_index]\n",
    "train_time = time_index[:split_index]  \n",
    "test_data = data[split_index:]\n",
    "test_time = time_index[split_index:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded1d0cb-2794-4500-a453-432335361c1d",
   "metadata": {},
   "source": [
    "##### 使用滑动窗口法构造训练数据\n",
    "\n",
    "![fig3](images/sliding_window_method.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "355e7470-3b1d-4777-8b6f-df01b632cd77",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-02 19:57:58.268040: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-02 19:59:15.615948: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "## required parameters\n",
    "WINDOW_SIZE = 60\n",
    "BATCH_SIZE = 32\n",
    "SHUFFLE_BUFFER = 1000\n",
    "\n",
    "## function to create the input features\n",
    "def ts_data_generator(data, window_size, batch_size, shuffle_buffer):\n",
    "    '''\n",
    "    Utility function for time series data generation in batches\n",
    "    '''\n",
    "    ts_data = tf.data.Dataset.from_tensor_slices(data)\n",
    "    ts_data = ts_data.window(window_size + 1, shift=1, drop_remainder=True)\n",
    "    ts_data = ts_data.flat_map(lambda window: window.batch(window_size + 1))\n",
    "    ts_data = ts_data.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
    "    ts_data = ts_data.batch(batch_size).prefetch(1)\n",
    "    return ts_data# Expanding data into tensors\n",
    "\n",
    "\n",
    "# Expanding data into tensors\n",
    "tensor_train_data = tf.expand_dims(train_data, axis=-1)\n",
    "tensor_test_data = tf.expand_dims(test_data, axis=-1)\n",
    "\n",
    "## generate input and output features for training and testing set\n",
    "tensor_train_dataset = ts_data_generator(tensor_train_data, WINDOW_SIZE, BATCH_SIZE, SHUFFLE_BUFFER)\n",
    "tensor_test_dataset = ts_data_generator(tensor_test_data, WINDOW_SIZE, BATCH_SIZE, SHUFFLE_BUFFER)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b8605a-b80e-4fdf-a7b3-e1b885c6788b",
   "metadata": {},
   "source": [
    "#### 使用 tf.keras 模块定义 1DConv+LSTM 神经网络模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ac6d6e5-fe06-4fa8-a2a6-ffe3e2c24b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "                            tf.keras.layers.Conv1D(filters=128, kernel_size=3, strides=1, input_shape=[None, 1]),\n",
    "                            tf.keras.layers.MaxPool1D(pool_size=2, strides=1),\n",
    "                        \ttf.keras.layers.LSTM(128, return_sequences=True),\n",
    "                        \ttf.keras.layers.LSTM(64, return_sequences=True),  \n",
    "                        \ttf.keras.layers.Dense(132, activation=\"relu\"),  \n",
    "                        \ttf.keras.layers.Dense(1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e326058-1e55-40af-a234-528ae48c211f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "81/81 [==============================] - 7s 65ms/step - loss: 7433.4023 - mae: 63.0034 - val_loss: 5102.1440 - val_mae: 57.1819\n",
      "Epoch 2/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 4611.1958 - mae: 54.1394 - val_loss: 4957.0796 - val_mae: 59.3796\n",
      "Epoch 3/20\n",
      "81/81 [==============================] - 5s 54ms/step - loss: 4616.9609 - mae: 54.6070 - val_loss: 4958.4634 - val_mae: 59.4598\n",
      "Epoch 4/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 4612.7168 - mae: 54.5015 - val_loss: 4955.5854 - val_mae: 59.3983\n",
      "Epoch 5/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 4602.3901 - mae: 54.3563 - val_loss: 4967.2427 - val_mae: 59.8981\n",
      "Epoch 6/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 4614.1133 - mae: 54.6332 - val_loss: 4937.0830 - val_mae: 59.5200\n",
      "Epoch 7/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 4538.5264 - mae: 53.9028 - val_loss: 4695.7964 - val_mae: 57.7491\n",
      "Epoch 8/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 3951.3457 - mae: 48.8506 - val_loss: 3303.1042 - val_mae: 47.4449\n",
      "Epoch 9/20\n",
      "81/81 [==============================] - 5s 55ms/step - loss: 3119.9194 - mae: 42.5141 - val_loss: 2522.4668 - val_mae: 40.9403\n",
      "Epoch 10/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 2707.9392 - mae: 39.1174 - val_loss: 2391.4512 - val_mae: 38.7295\n",
      "Epoch 11/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 2413.2185 - mae: 36.8490 - val_loss: 1966.1960 - val_mae: 35.5589\n",
      "Epoch 12/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 2062.7195 - mae: 34.0418 - val_loss: 2446.0183 - val_mae: 37.5970\n",
      "Epoch 13/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 1862.2104 - mae: 32.4633 - val_loss: 2209.2117 - val_mae: 34.4574\n",
      "Epoch 14/20\n",
      "81/81 [==============================] - 5s 60ms/step - loss: 1818.9036 - mae: 31.8016 - val_loss: 2393.9011 - val_mae: 36.7993\n",
      "Epoch 15/20\n",
      "81/81 [==============================] - 5s 57ms/step - loss: 1702.2509 - mae: 30.6973 - val_loss: 2445.1013 - val_mae: 36.6900\n",
      "Epoch 16/20\n",
      "81/81 [==============================] - 5s 60ms/step - loss: 1569.6904 - mae: 29.5193 - val_loss: 2315.6484 - val_mae: 35.7828\n",
      "Epoch 17/20\n",
      "81/81 [==============================] - 5s 54ms/step - loss: 1557.1240 - mae: 29.2358 - val_loss: 2693.0588 - val_mae: 37.0999\n",
      "Epoch 18/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 1596.0927 - mae: 29.5880 - val_loss: 2052.4604 - val_mae: 33.7434\n",
      "Epoch 19/20\n",
      "81/81 [==============================] - 4s 54ms/step - loss: 1421.5598 - mae: 27.9791 - val_loss: 2397.6289 - val_mae: 36.9736\n",
      "Epoch 20/20\n",
      "81/81 [==============================] - 5s 55ms/step - loss: 1360.6499 - mae: 27.1772 - val_loss: 2222.1130 - val_mae: 36.2328\n"
     ]
    }
   ],
   "source": [
    "## compile neural network model\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)\n",
    "model.compile(loss=\"mse\",\n",
    "          \toptimizer=optimizer,\n",
    "          \tmetrics=[\"mae\"])\n",
    "## training neural network model\n",
    "history = model.fit(tensor_train_dataset, epochs=20, validation_data=tensor_test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41c0bcda-50be-4fc7-8d79-67320f79cb82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63323850-d467-455f-b2a4-11038843d6c5",
   "metadata": {},
   "source": [
    "#### 使用训练好的模型预测 MSSN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "08c646a3-ce74-460d-bd31-0ca95660546c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101/101 [==============================] - 2s 18ms/step\n",
      "24.676455\n"
     ]
    }
   ],
   "source": [
    "def model_forecast(model, data, window_size):\n",
    "\tds = tf.data.Dataset.from_tensor_slices(data)\n",
    "\tds = ds.window(window_size, shift=1, drop_remainder=True)\n",
    "\tds = ds.flat_map(lambda w: w.batch(window_size))\n",
    "\tds = ds.batch(32).prefetch(1)\n",
    "\tforecast = model.predict(ds)\n",
    "\treturn forecast\n",
    "\n",
    "rnn_forecast = model_forecast(model, data[..., np.newaxis], WINDOW_SIZE)\n",
    "rnn_forecast = rnn_forecast[split_index - WINDOW_SIZE:-1, -1, 0]\n",
    "# Overall Error\n",
    "error = tf.keras.metrics.mean_absolute_error(test_data, rnn_forecast).numpy()\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e87c0d6-a9bc-4402-afc0-1687eb8df15a",
   "metadata": {},
   "source": [
    "#### 与真实值对比的可视化结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ac424d0-6e2a-4cee-8bb6-5d56872f38f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_data)\n",
    "plt.plot(rnn_forecast)\n",
    "plt.title('MSSN Forecast')\n",
    "plt.ylabel('MSSN')\n",
    "plt.xlabel('Month')\n",
    "plt.legend(['Ground Truth', 'Predictions'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ddaeac-a339-45d5-92d6-78e9a657e7c9",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
